Brain-computer interface for facilitating direct selection of multiple-choice answers and the identification of state changes

ABSTRACT

Methods, systems, apparatus, and non-transitory computer readable media are disclosed utilizing brain-computer interfaces (BCIs). Various embodiments are disclosed to allow a user to directly select multiple-choice answers, to provide motorized wheelchair controls, and to allow a user to play a game via the BCI. When used in a cognitive assessment test, embodiments include the administration of unmodified standardized tests with results in the same or a similar format as those taken without a BCI. Various embodiments are disclosed to improve the accuracy of BCI test administration using a three-step process for each test question, which includes determining whether the user intends to select an answer, monitoring user brain activity to determine a selected answer, and verifying the selected answer. In addition, the selected answer may be verified by monitoring user brain activity in accordance with a hold-release process to determine whether a user intends to initiate a state change.

CROSS-REFERENCE TO RELATED APPLICATION

This is the U.S. national phase of International Application No.PCT/US2015/032192, filed May 22, 2015. This application claims thepriority benefit under 35 U.S.C. § 119(e) of U.S. Provisional PatentApplication No. 62/005,243, filed May 30, 2014, the disclosure of whichis incorporated herein by reference in its entirety.

STATEMENT OF GOVERNMENTAL INTEREST

This invention was made with government support under TR000433,HD054913, and HD054697, awarded by the National Institutes of Health,and under H133G090005, awarded by the Department of Education. TheGovernment has certain rights in the invention.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems, methods, and apparatus for abrain-computer interface (BCI) and, more particularly, to a BCIimplementing a multi-step process to facilitate direct standardizedcognitive testing and the identification of a user's desired selectionsand changes to one or more selections, actions, and/or states.

BACKGROUND

For many patients with neurological conditions, cognitive assessmentsmay impact their quality of life by allowing medical personnel todetermine interventions and/or services that they may need to receive.But patients with neurological conditions may not be able to participatein such assessments due to motor and/or speech impairments. Furthermore,attempts to implement BCIs to administer cognitive assessment testing topatients with motor and/or speech impairments present several issues.

First, BCIs typically used for cognitive testing often implementindirect methods. For example, the BCI may allow a patient to move acursor on a screen to select a test question. Indirect methods do notprovide a patient with the precision and control necessary to quicklyselect an answer, and a patient may become distracted or frustratedduring the test, which may skew the test results. Second, andpotentially compounding these inaccuracies, indirect BCI cognitiveassessment testing procedures typically require that the cognitive testbe modified from the original standardized version to include theadaptive elements of indirect question selection. Therefore, providing acognitive assessment test that provides accurate results in accordancewith a standardized cognitive test format presents several challenges.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a brain-computer interface (BCI) testingsystem 100 in accordance with an exemplary embodiment of the presentdisclosure;

FIG. 2 illustrates a BCI device 200 in accordance with an exemplaryembodiment of the present disclosure;

FIG. 3A illustrates an example of a test question image prior to a usermaking an answer selection, in accordance with an exemplary embodimentof the present disclosure;

FIG. 3B illustrates an example of a test question image 320 used toverify a user's answer after BCI device 200 determines a user's answerselection, in accordance with an exemplary embodiment of the presentdisclosure;

FIG. 4 illustrates an example test answer selection method 400 inaccordance with an exemplary embodiment of the present disclosure; and

FIG. 5 illustrates an example hold-release state determination method500 in accordance with an exemplary embodiment of the presentdisclosure.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a brain-computer interface (BCI) system 100in accordance with an exemplary embodiment of the present disclosure.BCI system 100 may include a user 102, a brain activity monitoringsystem 103, a BCI 104, a display 106, and a test administrator 110.

As shown in FIG. 1, a user 102 may participate in a cognitive assessmenttest that is overseen by test administrator 110. The test administratormay assist in the test-taking procedure by, for example, accessing atest file from BCI 104, recording observations while the test is beingadministered to user 102, saving the answers to the test once it hasbeen completed, etc.

In some embodiments, BCI system 100 may be implemented as part of acognitive test assessment procedure. For example, BCI system 100 mayfacilitate the administration of one or more cognitive tests based onuser 102's brain activity and without utilizing motor and/or oralfeedback from user 102. Such an embodiment could be particularly usefulwhen user 102 is “locked-in” due to a specific impairment, and cannotreadily communicate otherwise in any viable physical manner.

The user's brain activity may include activity that is collected inresponse to a user being exposed to one or more stimuli, such as visualstimuli displayed on display 106 and/or other types of stimuli, such asauditory tones, etc. Stimuli other than those displayed via display 106are not shown in FIG. 1 for purposes of brevity. In an embodiment, testquestions may be displayed via display 106, as shown in FIG. 1, and eachmultiple-choice question may have an associated visual stimuliassociated therewith, which is further discussed below. Based upon ananalysis of user 102's brain activity while looking at (or otherwisepaying attention to, being exposed to, focusing on, concentrating on,etc.) a particular multiple choice answer, BCI 104 may determine user102's selection by correlating user 102's brain activity to theparticular unique visual stimuli associated with that answer.

In various embodiments, brain activity monitoring system 103 may beimplemented as one or more electroencephalograph (EEG) measurementdevices and may include any suitable number of electrodes and/orsensors. In accordance with such embodiments, the electrodes and/orsensors may be attached to any suitable portion of the user's head, etc.Various embodiments of brain activity monitoring system 103 may includeany combination of invasive and/or non-invasive electrode sensors. Brainactivity monitoring system 103 may be configured to measure a user'sbrain activity via any suitable number of electrodes and/or sensors inaccordance with any suitable number and/or type of standards, protocols,etc. Brain activity monitoring system 103 may be configured to convertand/or transmit the user's brain activity to BCI 104 as one or more datasignals in accordance with any suitable number and/or type ofcommunication formats, protocols, and/or standards, such as via link105, for example.

To provide another example, in accordance with an embodiment, brainactivity monitoring system 103 may be configured to measure a user'sbrain activity as one or more events within EEG bands such as Deltabands, Theta bands, Alpha bands, Beta bands, Gamma bands, and/or Mubands. In an embodiment, brain activity monitoring system 103 may beconfigured to monitor one or more event-related potential (ERP)components elicited by the user in the response to one or more choicespresented to the user via display 106.

BCI 104 may be implemented as any suitable device configured to receivedata signals from brain activity monitoring system 103, to analyzeand/or process these signals, and/or to transmit one or more datasignals to display 106 to provide feedback to user 102. For example, BCI104 may be implemented as a user equipment (UE), such as a mobiledevice, a computer, laptop, tablet, desktop, one or more parts of agaming system, one or more parts of a powered wheelchair controllersystem, one or more parts of any suitable device that is configured torender assistance to a user lacking motor and/or oral skills, or anyother suitable type of computing device.

Although shown in FIG. 1 as a single link 105, communications betweenBCI 104 and brain activity monitoring system 103 may be implemented withany appropriate combination of wired and/or wireless communicationnetworks, wires, buses, wireless links, etc., to facilitate thesecommunications. For example, BCI 104 and/or brain activity monitoringsystem 103 may utilize any combination of wired and/or wireless links,local area networks (LANs), etc.

As a result of processing and/or analyzing of the received data signalsfrom brain activity monitoring system 103, various embodiments includeBCI 104 facilitating the administration of a cognitive assessment testby performing one or more functions such as, for example, determininguser 102's intent to provide an answer to multiple-choice questionsdisplayed on display 106, determining user 102's answer selections,and/or verifying user 102's answer selections, which are furtherdiscussed below.

BCI 104 may be configured to transmit one or more data signals todisplay 106 and/or to another external generator of stimuli (not shownin FIG. 1) based upon these functions, such that display 106 may displaystimuli to the user corresponding to the multiple-choice questions, theuser's answer selections, and/or images to user 102 while user 102 istaking the test.

BCI 104 may be configured to transmit one or more data signals todisplay 106 to cause display 106 to display one or more images, tomodify the images, and/or to display additional images in response tothe measurements of the user's brain activity received from brainactivity monitoring system 103. For example, BCI 104 may determine user102's answer selection, for example, from data signals representative ofthe user's brain activity that are received from brain activitymonitoring system 103 while the user is exposed to a displayed stimulicorresponding to user 102's answer selection.

Display 106 may be configured to display information in response to theone or more data signals received from BCI 104, which may be receivedvia any suitable number and/or type of communication links (e.g., link107). Although display 106 is illustrated in FIG. 1 as being separatefrom BCI 104, various embodiments include display 106 being integratedas part of BCI 104, display 106 being co-located within, or proximateto, BCI 104, etc. As will be appreciated by those of ordinary skill inthe relevant art(s), the integration, coupling, and/or interactivefunctionality between BCI 104 and display 106 may depend on which ofthese implementations is utilized for a particular application.

Again, BCI 104 may be configured to cause display 106 to display one ormore test questions and/or audio prompts to determine a user's responseto these questions by analyzing and/or processing signals received frombrain activity monitoring system 103. In various embodiments, BCI 104may be configured to facilitate the cognitive assessment of a user inaccordance with any suitable cognitive test format, which may includestandardized or non-standardized tests. In accordance with embodimentsin which BCI 104 facilitates the administration of standardizedcognitive tests, BCI 104 may be configured to format the user's answersin accordance with the respective standardized test format. In this way,BCI 104 allows standard grading methods to be used for standardizedtests taken with BCI system 100.

In various cognitive test assessment embodiments, BCI 104 may beconfigured to process data signals received via brain activitymonitoring system 103 as part of a three-step process to determine user102's answer to the multiple-choice questions displayed on display 106.With regards to the analysis of the user's brain activity, variousembodiments may include BCI 104 executing one or more algorithms,instructions, programs, applications, code, etc., to facilitate thesefunctions. For example, BCI 104 may interpret signals received frombrain activity monitoring system 102 using classification systems suchas neural networks, stepwise linear discriminate analysis, supportvector machines, etc., to determine a probability that the user hasselected one of the displayed answers.

As the first step in this process, a determination may be made basedupon an analysis of data signals received via brain activity monitoringsystem 103 that user 102 intends to answer the multiple-choicequestions. That is, BCI 104 may be configured to analyze user 102'sbrain activity as part of this first step to ensure that user 102 isfocusing on taking the test, and is not distracted by external stimuliand/or not paying attention to the displayed images.

In other words, during the first step, BCI 104 may determine that user102 intends to decide upon an answer to the multiple-choice questionsdisplayed on display 106. In various embodiments, this first step may beimplemented using any suitable method to determine user 102's intention.For example, BCI 104 may implement one or more asynchronous BCIprocessing methods to make this determination.

To provide another example, during the first step, BCI 104 may wait fora predetermined period of time before accepting an answer from user 102.This time period, which may be indicated by a timer shown on display 106that does not block or otherwise interfere with the displayed images,may indicate an allotted time to allow user 102 to decide on an answerbefore the timer expires. In accordance with embodiments utilizing atimer for the determination of whether the user intends to decide uponan answer. BCI 104 may make this determination when the timer hasstarted.

In a second step, BCI 104 may determine user 102's answer from among thetest answers associated with the multiple-choice questions displayed ondisplay 106 once it has been decided that user 102 intends to provide ananswer. In various embodiments, BCI 104 may be configured to implementany suitable BCI process or combination of suitable BCI processes todetermine user 102's answer. For example, BCI 104 may generate imagesvia display 106 in accordance with a steady state visual invokedpotential process and analyze user 102's brain activity in accordancewith this process to determine user 102's selection.

To provide other examples, BCI 104 may analyze user 102's brain activityin accordance with P300 responses using any of a number of arrangementsfor displaying the image stimuli via display 106, such as a grid format,rapid serial visual presentation, etc.

In a third step, BCI 104 may verify the user's answer from the secondstep. In various embodiments, BCI 104 may continue to receive andanalyze the user's brain activity by executing one or more algorithms,instructions, programs, applications, code, etc., after the user'sselected answer has been determined by BCI 104 to verify the user'sanswer. For example, various embodiments include BCI 104 implementingerror potential detection, which may result in BCI 104 causing display106 to display an answer that was interpreted as chosen by the user, andthen determining whether the user's brain activity produced an errorpotential.

To provide another example, BCI 104 may cause display 106 to display oneor more images that allow user 102 to confirm or cancel the selectedanswer that been determined by BCI 104 in the second step, which isdisplayed to user 102 via display 106. To provide another example, BCI104 may cause display 106 to repeat the second step and compare theresults of both selections to verify a match.

To provide yet another example, BCI 104 may be configured to execute ahold-release algorithm with respect to two different states. The firstof these states may represent user 102 holding the initial answerselection, which is displayed to user 102 via display 106 after thesecond step. The second of these states may represent user 102 changinghis selection to another stimuli displayed via display 102 to cancel thedisplayed selection after the second step.

That is, embodiments include BCI 104 being configured to cause display106 to display the selected answer from step 2 and an image indicativeof the user's intent to cancel this answer selection. BCI 104 may beconfigured to execute a hold-release algorithm that associates theretention of the user's focus on the stimuli associated with thedisplayed answer as a hold state, and the transition of the userfocusing on the stimuli associated with the cancellation image as arelease state. The details of the hold-release process are furtherdiscussed below with reference to FIG. 5.

In this way, BCI system 100 may facilitate the administration ofstandardized and non-standardized testing via the monitoring of theuser's brain activity without the need for motor and/or oral feedbackfrom the user. In addition, BCI system 100 addresses many of the issuesregarding accuracy and standardization that typically plague indirectBCI testing procedures. Traditional BCI testing methods typically relyon an analysis of a user's brain activity to select a test answer usingindirect methods, such as by moving a cursor around a screen. Indirecttesting methods also have issues associated with accuracy and skewingtest results, including those related to a user becoming frustratedduring the test, which may compound errors and result in an incorrectassessment of the user's cognitive abilities.

In contrast to these indirect approaches, embodiments of BCI system 100allow user 102 to select answers in a direct way. This provides moreaccurate results compared to indirect methods, and also provides theadded benefit of not requiring a standardized test to be reformatted,which is generally required for indirect testing methods. In otherwords, direct selection by user 102 better conforms to the protocol forwhich the standardized test was designed, i.e., a direct selection ofmultiple-choice answers. By presenting the test in a similar way inwhich it was designed to be given to everyone (and not just userslacking motor and/or oral skills) BCI system 100 helps to remove testdata skewing that is otherwise introduced simply through the manner inwhich a BCI test is administered.

In other embodiments, BCI system 100 may be implemented as part of acontrol system configured to render assistance to a user lackingeffective motor and/or oral skills. For example, BCI 104 may beconfigured to additionally or alternatively use signals received viabrain activity monitoring system 103 and provide control commands tomotorized wheelchair 111. In accordance with such embodiments, BCI 104may be configured to transmit control commands to motorized wheelchair111 as one or more data signals in accordance with any suitable numberand/or type of communication formats, protocols, and/or standards, suchas via link 112, for example. Display 106 and/or BCI 104 may beintegrated as part of, mounted on, or otherwise associated withmotorized wheelchair 111 to facilitate these functions.

In still other embodiments, BCI system 100 may be implemented as part ofa gaming system playable by a user lacking effective motor and/or oralskills. For example, BCI 104 may be configured to additionally oralternatively use signals received via brain activity monitoring system103 and modify feedback displayed user 102 via display 106 as part of agaming application. In accordance with such embodiments, BCI 104 may beconfigured to transmit one or more data signals to display 106 inaccordance with any suitable number and/or type of communicationformats, protocols, and/or standards, such as via link 107, for example.

For example, BCI 104 may be configured to implement a hold-releasealgorithm for this purpose, with the hold state and the release statebeing associated with any suitable type and/or number of physicalactions, commands, etc., such as those used to control motorizedwheelchair 111, those used for a gaming application, etc. Similar to thedetermination of user 102's answers to test questions as previouslydiscussed, BCI 104 may analyze user 102's brain activity as the userfocuses on different stimuli displayed on display 106 corresponding tovarious controls. Based upon user 102's selected function, BCI 104 maydetermine whether user 102 would like to hold a selected command ortransition to a release state representing another command. Theseembodiments could be particularly useful in situations in which a userwants to use two different types of states to cause a change in acontrol process that may be represented in such a manner.

To provide an illustrative example, various embodiments include display106 displaying a particular stimulus for user 102 to focus on (e.g., bycounting flashes of an icon). When BCI 104 determines that user 102 isdoing so, BCI 104 may interpret the user's focus on the particularstimulus as a holding state, such as the activation of a motorizedwheelchair control, for example. The motorized control could beassociated with an action such as driving motorized wheelchair 111forward, backward, turning motorized wheelchair 111, etc. Continuingthis example, when BCI 104 detects user 102's initial selection that wasmade through attention to the stimulus, BCI 104 may cause a command tobe issued to motorized wheelchair 111 to drive forward and then maintainthat action as the holding state as long as user 102 continues to focuson the stimuli associated with that command.

Further continuing this example, when BCI 104 detects that user 102 hasswitched his focus to another stimulus (e.g., counting flashes ofanother icon), BCI 104 may interpret this as a de-activation or acancellation of a motorized wheelchair control, which represents arelease state. That is, the release state may be associated with thecessation of the action associated with the holding state. For example,if the holding state is associated with moving motorized wheelchair 111forward, then detection of the release state could cause BCI 104 toissue a command to stop motorized wheelchair 111.

In various embodiments in which BCI 104 executes hold-release statealgorithms, the algorithms may be applied to any suitable type and/ornumber of control states. Additional embodiments could includecontrolling volume by associating a volume increase (or decrease) with aholding state and the cessation of the holding state with the releasestate. In this way, BCI 104 may provide a user with the ability toexercise any type of control that takes advantage of state changes viaanalysis of a user's brain activity.

FIG. 2 illustrates a BCI device 200 in accordance with an exemplaryembodiment of the present disclosure. BCI device 200 includes a centralprocessing unit 202, a graphics processing unit (GPU) 204, acommunication unit 206, and a memory 208. BCI device 200 may beimplemented as any computing device suitable for receiving, monitoring,analyzing, and/or processing data signals representative of a user'sbrain activity. In an embodiment, BCI device 200 is an implementation ofBCI 104, as shown in FIG. 1.

In an embodiment, communication unit 206 may be configured to enable thereceipt of data from a brain activity monitoring system, such as frombrain activity monitoring system 103, for example, as shown in FIG. 1.In various embodiments, communication unit 206 may be configured tofacilitate the transfer of data received from a brain activitymonitoring system to CPU 202 and/or to memory 208. For example, datareceived from communication unit 206 from a brain activity monitoringsystem may be stored in any suitable location in memory 208 forsubsequent processing by CPU 202.

Alternatively or additionally, various embodiments of communication unit206 include communication unit 206 sending one or more commands,signals, data, etc., to one or more control components to facilitate astate change. Examples of control components could include motorcontrollers, volume controllers, or any suitable type of controllercomponent that may be utilized to assist a user with impaired motorand/or oral skills. These control components are not shown in FIG. 2 forpurposes of brevity.

As will be appreciated by those of skill in the relevant art(s),communication unit 206 may be implemented with any combination ofsuitable hardware and/or software to enable these functions. Forexample, communication unit 206 may be implemented with any number ofwired and/or wireless transceivers, network interfaces, physical layers(PHY), etc.

In various embodiments, CPU 202 and/or GPU 204 may be configured tocommunicate with memory 208 to store to and read data from memory 208.For example, CPU 202 and/or GPU 204 may be implemented as any suitablenumber and/or type of processors. In various embodiments, CPU 202 may beconfigured to process brain activity data signals received from a brainactivity monitoring system, while GPU 204 may be configured to send datasignals and/or commands to a display device, such as display 106, forexample, as shown in FIG. 1, to cause the display to show one or moreimages. In an embodiment, the images that GPU 204 causes to be displayedare used to administer a cognitive assessment test, such as thosepreviously discussed with reference to FIG. 1.

In accordance with various embodiments, memory 208 is acomputer-readable non-transitory storage device that may include anycombination of volatile (e.g., a random access memory (RAM), or anon-volatile memory (e.g., battery-backed RAM, FLASH, etc.). In variousembodiments, memory 208 may be configured to store instructionsexecutable on CPU 208 and/or GPU 204. These instructions may includemachine readable instructions that, when executed by CPU 202 and/or GPU204, cause CPU 202 and/or GPU 204 to perform various acts.

In various embodiments, data read/write module 210, hold-release module212, and BCI processing and testing module 214 are portions of memory208 configured to store instructions executable by CPU 202 and/or GPU204. In various embodiments, data read/write module 210 may includeinstructions that, when executed by CPU 202 and/or GPU 204, causes CPU202 and/or GPU 204 to read data from and/or to write data to memory 208.In various embodiments, data read/write module 210 may includeinstructions that, when executed by CPU 202 and/or GPU 204, causes CPU202 and/or GPU 204 to receive data from a brain activity monitoringsystem via communication unit 206. In an embodiment, data read/writemodule 210 may enable CPU 202 and/or GPU 204 to access, read, and/orexecute one or more one or more algorithms, instructions, programs,applications, code, etc., stored in hold-release module 212 and/or BCIprocessing and testing module 214.

In various embodiments, BCI processing and testing module 214 may beconfigured to store one or more algorithms, instructions, programs,applications, code, etc., that are executed by CPU 202 and/or GPU 204 aspart of an overall framework process. In some embodiments, thisframework process includes the data processing instructions inaccordance with a particular type of BCI. For example, when a test isadministered to a user in a BCI format, the brain activity data signalsfor that user may be processed and analyzed in accordance with one ormore types of BCI protocols. In various embodiments, BCI processing andtesting module 214 may be configured to store instructions regardingthis formatting, and how to process signals received from a brainactivity monitoring system in accordance with one or more formats tointerpret the user's intentions, selections, and/or decisions as theuser is exposed to various stimuli.

For example, as previously discussed with reference to FIG. 1, variousembodiments include BCI device 200 executing a three-step process foreach test question to ensure that the user's selected answer isaccurate. During each of these steps, BCI device 200 may cause images tobe displayed to a user, via GPU 204, and to receive, via communicationunit 206, data signals from a brain activity monitoring system inresponse to the user viewing stimuli associated with these images.

In an embodiment, BCI processing and testing module 214 may beconfigured to store instructions including the type of stimuli and/orimages sent to display 206 and how CPU 202 processes signals receivedfrom a brain activity monitoring system in response to the user viewingthese stimuli and/or images. For example, if a user's intention isdetermined in step one of the three-step process via an asynchronous BCIprocess, then BCI processing and testing module 214 may be configured tostore instructions read by CPU 202 to process received brain activitysignals in accordance with that asynchronous BCI process.

To provide another example, embodiments include the second step in thetesting process determining a user's answer selection. Several types ofbrain activity processes may be implemented to facilitate thisdetermination. For example, if steady-state visually evoked potentials(SSVEP) are implemented, GPU 204 may send images representative of testanswers to a display (e.g., display 106). Based on the feedback receivedfrom data signals indicative of the user's brain activity, BCIprocessing and testing module 214 may include instructions regarding howto process this feedback in accordance with the SSVEP process toidentify the displayed image that the user intends as an answer and/orto modify the displayed images to indicate the user's selected answer.

Furthermore, in various embodiments, BCI processing and testing module214 may be configured to store instructions including the testquestions, answer keys, user answers, and/or images representative ofthe test questions themselves. In various embodiments, BCI processingand testing module 214 may store any suitable number of tests, which maybe administered when selected by an operator, such as by medical staffadministering the test, for example. In various embodiments, an operator(e.g., medical staff member) may alter the contents of BCI processingand testing module 214 by uploading new tests and/or downloading testanswers.

In an embodiment, BCI processing and testing module 214 may beconfigured to store instructions enabling CPU 202 to store a user'sselected answers for any suitable number of test questions as a testanswer profile. In an embodiment, the test profile may be generated byCPU 202 after the three-step process is applied to each test question.For example, the test answer profile could be an answer profile thatconforms to a standard test key grading system, such as a listing ofmultiple-choice answers for each test question. In this way, once astandardized test is administered via BCI device 200, the answers tothat test may be graded in accordance with the standard test answer key,greatly reducing grading errors that could otherwise be introduced whenadapting the test for compatibility with the BCI test procedure.

In various embodiments, hold-release module 212 may be configured tostore one or more algorithms, instructions, programs, applications,code, etc., that are executed by CPU 202 and/or GPU 204 to facilitatehold-release functionality, which will be further discussed below withreference to FIGS. 3A-B. For example, hold-release module 212 mayinclude executable code in any suitable language and/or format. In someembodiments, hold-release module 212 may be configured to includeinstructions that are executed in conjunction with the third step in thethree-step process that is applied during one or more questions for theadministration of a cognitive test assessment, as previously discussedwith respect to FIG. 1.

In other embodiments, hold-release module 212 may be configured toinclude instructions that are executed in conjunction with a hold andrelease control state change and may be used alternatively or inaddition to a testing process. Again, further details regardingimplementing the hold and release process for identifying and/orcontrolling state changes are discussed below with respect to FIGS.3A-B.

Although FIG. 2 illustrates communication unit 206, CPU 202, GPU 204,and memory 208 as separate elements, various embodiments of BCI device200 include any portion of communication unit 206, CPU 202, GPU 204, andmemory 208 being combined, integrated, and/or separate from one another.For example, any of communication unit 206, CPU 202, GPU 204, and memory208 could be integrated as a single device, a system on a chip (SoC), anapplication specific integrated circuit (ASIC), etc.

Furthermore, although data read/write module 210, hold-release module212, and BCI processing and testing module 214 are illustrated asseparate portions of memory 208, various embodiments include thesememory modules being stored in any suitable portion of memory 208, in amemory implemented as part of CPU 202 and/or GPU 204, and/or spreadacross more than one memory. For example, data read/write module 208could be stored as part of memory 208, while hold-release module 212 andBCI processing and testing module 214 are stored in a memory integratedas a part of CPU 202. As will be appreciated by those of ordinary skillin the relevant art(s), different memory modules may be integrated as apart of CPU 202 to increase processing speed, reduce latency and/ordelays due to data processing bottlenecks, etc. For purposes of brevity,only a single memory 208 is illustrated in FIG. 2.

Although illustrated as a single BCI device in FIG. 2, in variousembodiments BCI device 200 may consist of any number or group of one ormore BCI devices. In accordance with such embodiments, each BCI devicemay include one or more CPUs and be configured to operate independentlyof the other BCI devices. BCI devices operating as a group may processsignals received from a brain activity monitoring system individually(e.g., based on their availability) and/or concurrently (e.g., parallelprocessing).

FIG. 3A illustrates an example of a test question image 300 prior to auser making an answer selection, in accordance with an exemplaryembodiment of the present disclosure. Test question image 300 includesfour multiple-choice answer selections 302, 304, 306, and 308. Each ofthe four multiple-choice answer selections 302, 304, 306, and 308 alsohas an associated label 310A-D, respectively. Although FIG. 3Aillustrates labels 310A-D as numbers 1-4, respectively, any suitabletype of identifier may be used as labels 310A-D, such as letters, forexample.

As shown in FIG. 3A, each of labels 310A-D is surrounded by a respectiveborder pattern 311A-D, which may include a black and white checkerboardpattern, for example. In accordance with various embodiments, the blackand white checkerboard patterns constituting each border patternalternate patterns of black and white to “flicker” at a correspondingfrequency. Several BCI methods are typically used to determine a user'sdecisions when exposed to these types of visual stimuli.

For example, using one type of BCI method, SSVEP, each of borderpatterns 311A-D may flicker at a different frequency than one another,and these frequencies may be high enough such that the flicker is notconsciously counted by the user viewing the images. Nonetheless, theflicker frequency is able to be identified via an analysis of the user'sbrain activity data signals received at a BCI, such as BCI device 200,for example, while a user is viewing a selected answer image and itsassociated border pattern and label.

Although FIG. 3A illustrates each of border patterns 311A-D as having ablack and white checkerboard pattern, any suitable pattern type may beused for border patterns 311A-D. For example, border patterns 311A-D maybe implemented as having any suitable type of color, pattern, design,etc., which may be used to provide suitable SSVEP stimuli. For example,border patterns 311A-D may include a solid, single color that flickersat a particular frequency.

In another type of BCI method, P300, or event-related potential (ERP)BCI, each of labels 310A-D may flash at a slower rate, which may becounted by the user, and an analysis of the user's brain activity datasignals received at a BCI, such as BCI device 200, for example, mayindicate a positive change in the user's brain activity data signalsabout 300 milliseconds after each flash occurs. Identification of thispositive change, whose timing may be specified for individual users,allows for the identification of the answer selection that the userintends to choose corresponding to each of respective labels 310A-D.Additionally or alternatively, various embodiments include any suitableportion of images 302, 304, 306, and 308 flashing, such as the imagesthemselves.

Although FIG. 3A illustrates several labels 310A-D and theircorresponding border patterns, embodiments include images 302, 304, 306,and 308 flickering in accordance with SSVEP frequencies or flashing inaccordance with a P300 flash pattern. In accordance with suchembodiments, labels 310A-D (and border patterns 311A-D) may be omittedand user 102 may be instructed, for example, that each test answerposition corresponds to one of answers A-D for the duration of the test.

In various embodiments, each of border patterns 311A-D may flicker inaccordance with SSVEP frequencies, each of labels 310A-D may flash inaccordance with a P300 flash pattern, each of images 302, 304, 306, and308 may flicker in accordance with SSVEP frequencies or flash inaccordance with a P300 flash pattern, or any combination of flickeringand/or flashing may occur among each of border patterns 311A-D, labels310A-D, and/or images 302, 304, 306, and 308 may happen simultaneously.

For example, in an embodiment, border patterns 311A-D may flicker at aSSVEP frequency to allow BCI device 200 to process a user's brainactivity data signals to assess the flicker frequency with a desiredanswer selection, while labels 310A-D may flash at the same time inaccordance with a P300 flash pattern, additionally registering theuser's recognition response to making the desired selection inaccordance with a P300 BCI process.

Various embodiments of BCI device 200 may be implemented for theadministration of any suitable test. However, the example image 300shown in FIGS. 3A-B may correspond to one or more images such as thoseused in a PEABODY PICTURE VOCABULARY TEST—4^(TH) EDITION (PPTV-IV), forexample. The PPTV-IV test includes an oral pronunciation of a word, andallows a user to select the image that most closely resembles that word.For purposes of explanation, assume that “bird” is the correct answer toa test question represented by example image 308.

When a test is administered in accordance with various embodiments, theuser would focus on label 310D and its corresponding border pattern 311Dthat are associated with image 308 to select this answer. In anembodiment, the three-step process may be applied to determine theuser's selected answer.

As previously discussed, the first step in the three-step process isascertaining whether a user is paying attention to the displayed answersor intends to answer the test question. In accordance with such anembodiment, FIG. 3A is an example of what may be displayed to a userduring the first and second steps of such a three-step process. Forexample, if a timer is used to verify that a user is ready to decideupon an answer, timer 313 may be displayed indicating that the test hasbegun and a remaining time for the user to select an answer. To provideanother example, a tone or other notification may be used to indicate atimer has started, which may or may not be visible to the user while theuser prepares to make a decision.

In an embodiment, the second step may begin once timer 313 has started(or until an asynchronous BCI process, for example, otherwise indicatesthat the user intends to answer, until a threshold amount of time isleft on the timer, etc.). That is, each respective answer selection'sborder pattern 311 and/or label 310 may being flickering before timer313 starts, but BCI device 200 may wait until a determination that theuser actually intends to answer before processing the user's brainactivity data signals. Based on a monitoring of the user's brainactivity data signals, BCI device 200 may then determine the user'sselected answer, such as the answer associated with image 308, forexample. Once BCI device 200 determines that the user has selected theanswer associated with image 308, the image is modified to the imagethat is shown in FIG. 3B, which is further discussed below. By waitinguntil it is determined that the user intends to select an answer in thisway, embodiments of BCI device 200 help to ensure that the answerdetermined during the second step is correct.

FIG. 3B illustrates an example of a test question image 320 used toverify a user's answer after BCI device 200 determines a user's answerselection, in accordance with an exemplary embodiment of the presentdisclosure. In an embodiment, image 320, as shown in FIG. 3B, is shownto a user in accordance with the third step in the three-step answerselection process. That is, once a determination has been made by BCIdevice 200 that the user has selected answer image 308 from FIG. 3A,answer image 308 is maintained while the remaining images 302, 304, and306 are de-emphasized in FIG. 3B. In various embodiments, thisde-emphasizing may be implemented by any suitable methods such asfading, muting, removing, reducing, adjusting colors, etc., associatedwith the non-selected answer images.

In addition to the de-emphasis of the other answer images, embodimentsalso include the presentation of a cancellation image, an example ofwhich is shown in FIG. 3B as cancellation image 312. Based on theparticular BCI method used (e.g., P300, SSVEP, or both) cancellationimage 312 may also include a border pattern 314. Similar to labels310A-310D and border patterns 311A-D, cancellation image 312 and/orborder pattern 314 may flicker at a specific SSVEP frequency and/orflash in accordance with a P300 flash pattern (e.g., as a sequence ofstimuli that is part of a P300 flash sequence to illicit a P300 ERP).For example, cancellation image 312 may flash in accordance with aparticular P300 flash pattern, border pattern 314 may flicker at aparticular SSVEP frequency, or both.

Similar to border patterns 311A-D, various embodiments include borderpattern 314 implemented as any suitable type of color, pattern, design,etc., which may be used to provide a suitable SSVEP stimuli. Once theuser is presented with the image as shown in FIG. 3B, the user has twooptions. If the user intends to keep the selected answer correspondingto image 308, the user can maintain her focus on image 308. But if theuser accidentally chose image 308 as the wrong selection (or if BCIdevice 200 misinterpreted the user's selection) then the user may switchher concentration to image 312 to cancel this selection. In accordancewith various embodiments, BCI device 200 may be configured to detectwhether the user intends to hold the selected answer image 308 or tochange (i.e., release) from the holding state by focusing oncancellation image 312.

In an embodiment, BCI device 200 may be configured to present anysuitable number of images (e.g., four, as in FIGS. 3A-3B) correspondingto the next test question if a holding state is detected, i.e., if BCIdevice 200 detects that the user is maintaining focus on image 308.Further in accordance with such an embodiment, BCI device 200 may beconfigured to replace FIG. 3B with the image shown in FIG. 3A if arelease state is detected, i.e., if BCI device 200 detects that the userhas switched his focus from image 308 to cancellation image 312.

In an embodiment, this process may be repeated any suitable number oftimes for each test question until an answer is obtained for allquestions in the test. Once the test answers are collected, BCI device200 may build and/or format a corresponding answer profile for the userthat may be graded in accordance with a test answer key. For example,since each answer image has a corresponding number, test answers may becollected by identifying the answer images by number. For the previousexample, once the user's selection of answer image 308 was verified, theanswer number “4” may be recorded for that test question.

In various embodiments, the hold-release state transition detection maybe implemented in a number of sub-steps as part of the third step in thethree-step answer selection process. In an embodiment, training data maybe collected for a particular user prior to running the test (or othersystem in which BCI device 200 is implemented). For example, thedetermination made by BCI device 200 regarding a user's particularselection is based upon the user's brain activity data signals, but istypically not an absolute certainty; rather, the decisions are typicallyperformed in the context of a mathematical analysis.

In other words, because each user's brain activity is unique anddifficult to measure, embodiments include BCI device 200 determining auser's selections by weighting the importance of portions of the brainactivity data signals considered most highly correlated with thedecision. For example, weights for individual portions of the brainactivity data signals may be determined based on the classification ofcollected brain signal activity in response to a user being exposed to aparticular set of known stimuli. These user may be exposed to the knownstimuli through the training process, for example. The weights may becalculated via a classification process, resulting in a range ofclassifier values corresponding to each type of stimuli for SSVEP, tothe presence or absence of evoked potentials such as the P300 ERP, etc.

For example, a selection classifier training process may be implementedfor a particular user before a test is administered. The selectionclassifier training may correspond to the user viewing and/orconcentrating on several different stimuli (e.g., border portions311A-D) that flicker at various frequencies. Based on this trainingdata, different ranges of classifier values may be calculated by BCIdevice 200 based on a user's brain activity while the user is exposed todifferent stimuli.

Once the selection classifier training process has been completed, BCIdevice 200 may calculate new (i.e., post-training) classifier valuesbased upon the user's brain activity data signals during subsequentexposures to the same stimuli. These new classifier values may becompared to the different ranges of classifier values calculated duringthe selection classifier training process, to one another, and/or to oneor more threshold values, which is further discussed below, to identifywhich of the subsequent stimuli the user is being exposed to (e.g.,which image the user is focusing on). The various stimuli may correspondto one or more answer selections, actions, etc. Through an analysis ofthe user's brain activity data and updated classifier values,embodiments include BCI device 200 determining the user's decision toeither hold the selected answer or to release the answer (i.e., cancelit).

In various embodiments, any suitable number of rules may be constructedto ensure that the user's decisions are accurately determined. Forexample, after the training data has been collected, BCI device 200 maycontinue to monitor the user's brain activity (i.e., receive and processthe user's EEG data signals) while the user focuses on a particularstimuli after a selection has been made. This could include, forexample, a user continuing to focus on stimuli provided by label 311Band border portion 310B of answer image 308, or switching his focus toborder portion 314 associated with cancellation image 312, as shown inFIG. 3B. In an embodiment, BCI device 200 may be configured to generateanother, subsequent classifier—a hold-release classifier, based on acomparison between (1) classifier values calculated during themonitoring of the user's brain activity after the user has made aselection, and (2) the range of hold-release classifier values that havebeen determined prior to testing, which may be referred to as “trainingclassifier values,” throughout this disclosure.

To provide an illustrative example, BCI device 200 may first calculatetwo threshold classifier values that separate the training classifiervalues associated with the user viewing a target stimulus and theclassifier values associate with the user viewing an irrelevantstimulus. Continuing this example, during the selection classifiertraining process, a user may be instructed to consider one image (e.g.,308) as the correct answer from a set of images (target stimulus). Theuser may also be instructed to focus on cancellation image 314(cancellation stimulus). Flashes of this image's respective label 310Band/or flickering of its border portion 311B would then be consideredthe target stimuli, while flashes of cancellation image 312 and/orflickering of border 314 would be considered the cancellation stimuli.

During the training process, the user's brain activity in response tothe target and cancellation stimuli may be used to calculate weights fora classifier in accordance with any suitable classification method, suchas a least squares regression analysis, for example. Application ofthese weights to an individual's brain activity data signals wouldproduce classifier values. As a result of the selection classifiertraining process, one range of classifier values would be identified asassociated with the target stimuli while another range of classifiervalues would be associated with non-relevant stimuli.

In an embodiment, thresholds at the border of these ranges may be usedas an indication of whether a new classifier value (e.g., fromsubsequent exposure to either target or cancellation stimuli) should beconsidered to be the result of a user's exposure to a target stimuli, toa cancellation stimuli, or remain unknown. In various embodiments, adetermination of an unknown response may be further analyzed withadditional rules, as further discussed below, to determine whether thenew classifier value should be considered to be in response to a user'sexposure to a target or a cancellation stimuli.

Further expanding upon this exemplary rule, BCI 200 may identify variousstimuli, from classifier values calculated using the user's brainactivity data signals while exposed to the stimuli, based upon acomparison between the subsequently calculated classifier values and thecorresponding range of target classifier values.

Furthermore, in accordance with an embodiment, BCI device 200 may usethe largest classifier values to identify a target stimuli selection bythe user. But since the hold-release decision with reference to FIG. 3Bis only with regards to two possible choices, only classifier valueranges for the brain activity response to answer stimuli associated withimage 308 and cancellation image 312 are required. That is, if theselection classifier training process resulted in the calculation of arange of classifier values corresponding to values designated as C1-C10for a target stimuli selection by the user, and classifier value rangesdesignated as C20-C30 for cancellation stimuli selection by the user,the rule could set one or more threshold classifier values to separatethe ranges (C1-C10) and (C20-C30).

Using this rule, a user's intention to hold the selected answer image308 in FIG. 3B could be determined by BCI device 200 when theclassification of the user's brain activity data signals results in aclassifier value equal to or greater than a threshold classifier value(e.g., C15) such that classifier values falling above or below the oneor more threshold values are associated with the user either continuingto focus to the target stimuli or switching to the cancellation stimuli.In various embodiments, any suitable threshold classifier value may beutilized, such as a threshold classifier at the lower end of C1-C10, aclassifier threshold value at the upper end of C20-C30, zero, etc.

Another example of a rule may include comparing a classifier valuecalculated from the user's brain activity data signals during the thirdstep with a predetermined classifier value. For example, if a classifiervalue associated with image 308 during the third step is a negativevalue (assuming zero was determined from the training process classifiervalues as a baseline below which classifier values are associated withcancellation stimuli) then BCI device 200 may determine that the userhas decided to select cancellation image 312 instead of answer image308. In other words, in this rule example, a negative classifier valueindicates a negative correlation to a user's intention to hold theselected answer image 308, and therefore the hold state is switched tocancellation image 312.

When implementing such a rule, BCI device 200 may determine, forexample, if one or more conditions are met, and identify the user'sdecision (i.e., the appropriate holding state) based on any suitablecombination of these conditions being satisfied. For example, BCI device200 may implement a three-part rule. An example of the three-part rulecould include BCI device 200 first determining which of the twoclassifier values is larger than the other. Using a typical classifiersystem, a higher classifier value is typically associated with a highercorrelation between the user's decisions to hold one state (e.g., theselected image 308) versus another state (e.g., cancellation image 312).Second, BCI device 200 may then determine whether the first and secondclassifier values are both positive, which could indicate a bettercorrelation between the user intending to select either one of thestates. Third, BCI device 200 may determine whether the first and secondclassifier values are both less than a threshold value, such as thethreshold value as previously discussed with respect to the first rule,for example. If all three rule conditions are satisfied in the examplethird rule, BCI device 200 may identify the state associated with thehigher classifier value as the holding state.

Furthermore, although the hold and release process has been described interms of a single hold and a single release state mapped to individualcontrol states, various embodiments include any suitable combination ofvarious hold and/or release states. For example, a user could be exposedto any number of stimuli associated with respective holding states and asingle release state that stops the activity associated with thecurrently selected holding state. Such embodiments could be particularlyuseful when, for example, it is desirable to provide a user with accessto multiple holding states that may be used to provide more complextypes of control, such as turning, increasing speed, decreasing speed,etc., that form part of a singularly controlled device.

Although the details of the hold-release concept have been explainedwith reference to a user selecting answers to test questions,embodiments include BCI device 200 implementing the hold-releasefunctionality as part of any suitable system that utilizes statechanges. That is, a holding state may be identified with any state theuser wishes to maintain, while the release state may be associated withany state that results from the user's desire to stop the holding state.

To provide another example, the hold and release states could be appliedto motor controls for a motor-powered wheelchair, or any other suitabletype of motored assisting device. In such an embodiment, the holdingstate could be associated with a forward movement or a turn, while therelease state could be associated with the stoppage of the movement orturn. In addition, embodiments include the hold and release statesswitching their associated mapped control behaviors for a particularapplication. As previously discussed, the detection and switching of theidentification of hold and release states could be especially useful insuch embodiments. That is, a user may wish to quickly switch betweenmoving forward, stopping, and then moving forward again. In such anexample, embodiments include the holding state initially beingidentified as the movement state and the release state initially beingidentified as the stopping state. Once the user decides to stop hismovement, the holding state could then be identified as the stoppedstate, and the released state identified as the movement state. In anembodiment, these states could continuously switch to allow a user'sdesired decisions to be interpreted quickly and accurately.

In yet another example, the hold-release states could be applied to anysuitable type of speller used to provide or supplement an impaireduser's speech. In such an embodiments, any suitable number of hold andrelease states could be associated with any suitable number oflocations, rows, columns, etc., of a BCI speller. The hold and releasesystem could be implemented in the context of a BCI speller byinterpreting a user's intention to select one or more locations with aholding state, and providing a cancelation image to release theselection in the case of an erroneous selection, interpreting continuedattention to the location as a hold and confirmation.

FIG. 4 illustrates an example test answer selection method 400 inaccordance with an exemplary embodiment of the present disclosure. Inthe present embodiment, method 400 may be implemented by any suitablecomputing device (e.g., BCI device 104 or BCI device 200, as shown inFIGS. 1 and 2, respectively). In one aspect, method 400 may be performedby one or more algorithms, instructions, programs, applications, code,etc., such as any suitable portion of CPU 202 executing instructions inone or more of the modules stored in memory 208, for example, as shownin FIG. 2.

Method 400 may begin when one or more processors display imagescorresponding to multiple-choice answers for the cognitive assessmenttest (block 402). This may include, for example, displaying images inaccordance with a standardized test format, such as a standardizedcognitive assessment test used to measure a user's cognitive abilities,in an embodiment (block 402). In an embodiment, the images may begenerated, for example, by one or more GPUs of a BCI device, such as GPU204, as shown in FIG. 2, for example. The images may include, forexample, images corresponding to the images shown in FIG. 3A that areretrieved from CPU 202 and/or GPU 204 from one or more portions ofmemory 208, which could include retrieval of one or more saved filesfrom BCI processing and testing module 214, for example (block 402).

Method 400 may include one or more processors receiving EEG signalsbased upon a user's brain activity during administration of a cognitiveassessment test (block 404). The EEG signals may be generated, forexample, via any suitable brain activity monitoring system configured tomeasure the user's brain activity, such as brain activity monitoringsystem 103, for example, as shown in FIG. 1 (block 404).

Method 400 may include one or more processors determining whether a userintends to decide upon an answer from the multiple-choice answers (block406). This determination could be made, for example, by one or more CPUsof a BCI device, such as CPU 202, as shown in FIG. 2, for example, in anembodiment (block 406). For example, this determination may be made whenone or more processors display a timer to inform a user to decide uponan answer before time runs out, such as timer 313, for example, as shownin FIG. 3A (block 406). To provide another example, this determinationmay be made via an asynchronous BCI process performed on the user's EEGsignals (block 406) while taking the test.

If the one or more processors determine that the user is ready to decideupon an answer, method 400 proceeds to determine the user's answer(block 408). Otherwise, method 400 continues to wait for the user to beready to decide upon a displayed answer image (block 406). In anembodiment, the determination of whether the user is ready to decideupon an answer corresponds to the first step in a three-step answerselection and verification process (block 406).

Method 400 may include one or more processors determining the user'sselection from the multiple-choice answers (block 408). In anembodiment, the determination of the user's selection is part of asecond step in a three-step answer selection and verification process(block 408). This determination may include, for example, monitoring theuser's brain activity data signals (e.g., EEG signals) in response tothe user being presented with the displayed images (block 402) inaccordance with an SSVEP BCI and/or a P300 BCI process, in variousembodiments (block 408).

Method 400 may include one or more processors determining whether theuser's answer (block 408) has changed (block 410). This may include, forexample, one or more processors continuing to receive EEG signals fromthe user after the determination of the user's selected answer (block408) to verify whether the user's brain activity indicates a match tothe user's previously selected answer (block 410). In an embodiment, theverification of the user's answer (block 408) is part of a third step ina three-step answer selection and verification process (block 410).

In the present embodiment, method 400 may include verifying the user'sanswer (block 408) by one or more processors modifying the displayedimages (block 402) to de-emphasize other answer selections whilepresenting a cancellation image, as shown in FIG. 3B (block 410). Thismay also include, for example, one or more processors processing theuser's brain activity to determine whether the user's selected answercorresponds to a current holding state, or whether the user's brainactivity indicates the user's intention to cancel the selected answerthrough the identification of a release state associate with the user'sfocus on the cancellation image (block 410). If the user's selectedanswer is verified and/or the release state is not detected, method 400continues to record the user's selected answer (block 410). Otherwise,method 400 reverts back to displaying the initial images presented tothe user prior to the user making the selection (block 402).

Method 400 may include one or more processors recording the user'sanswer (block 412). This may include, for example, one or moreprocessors, such as CPU 202 as shown in FIG. 2, for example, storing theuser's verified selected answer (block 410) in a memory, such as memory208, for example, in an embodiment (block 412).

Method 400 may include one or more processors advancing to the next testquestion (block 414). This may include, for example, one or moreprocessors, such as CPU 202 as shown in FIG. 2, for example, retrievingthe next test question from testing and processing module 214 of memory208, for example, in an embodiment (block 414). If the last testquestion was recorded, method 400 may include one or more processorsformatting and/or storing the entire user answer profile in a memory,such as memory 208, as shown in FIG. 2, for example (block 414). Oncethe next test question is advanced at block 412, method 400 may includedisplaying the next test question to the user (block 402).

FIG. 5 illustrates an example hold-release state determination method500 in accordance with an exemplary embodiment of the presentdisclosure. In the present embodiment, method 500 may be implemented byany suitable computing device (e.g., BCI device 104 or BCI device 200,as shown in FIGS. 1 and 2, respectively). In one aspect, method 500 maybe performed by one or more algorithms, instructions, programs,applications, code, etc., such as any suitable portion of CPU 202executing instructions in one or more of the modules stored in memory208, for example, as shown in FIG. 2. In an embodiment, method 500 is animplementation of the third verification step in a three-step process,as previously discussed with reference to FIG. 3B, for example.

Method 500 may begin when one or more processors calculate a first and asecond range of classifier values based upon a user's EEG signals (block502). In an embodiment, the first and second range of classifier valuesmay be calculated based upon the user's exposure to target andcancellation stimuli, respectively (block 502).

For example, the first and a second range of classifier values may becalculated as training classifier values during a training sessionwhereby the user is exposed to various stimuli that may be associatedwith various applications of a BCI, such as administration of acognitive test, a speller, controls for a motor-powered wheelchair, etc.(block 502). For example, the first and second stimuli could include auser's exposure to stimuli associated with a selected test answer imageand a cancellation image, such as a flickering borders 310A-D and/orborder 314 as shown in FIG. 3B, respectively, in an embodiment.

Method 500 may include one or more processors calculating a first and asecond training classifier threshold (block 504). In an embodiment, thefirst and second training classifier thresholds may be calculated basedupon the first and the second range of classifier values, such that thefirst and second training classifier threshold separate the first andthe second range of classifier values from one another (block 504).

Method 500 may include one or more processors classifying received EEGsignals while the user is subsequently exposed to the target or thecancellation stimuli (block 506). This classification may include, forexample, classifying the signals as being within the first or the secondrange of classifier values based upon the first and a second trainingclassifier thresholds (block 506).

Method 500 may include one or more processors determining whether theuser has been exposed to the target stimuli or to the cancellationstimuli based upon the classifying of the subsequently received EEGsignals into one of the first or the second range of classifier values(block 508). This may include, for example, comparing the classified EEGsignals corresponding to determine which of the first or the secondrange of classifier values the classified EEG signals fall within (block508).

Method 500 may include one or more processors calculating a hold-releaseclassifier value based upon EEG signals received after determining(block 508) whether the user has been subsequently exposed to the targetstimuli or to the cancellation stimuli (block 510). The calculation mayinclude, for example, classifying the user's brain activity (e.g., EEGsignals) using any suitable techniques as previously discussed withrespect to FIG. 3B to generate the hold-release classifier value (block510).

Method 500 may include one or more processors identifying whether theuser has decided to hold an action associated with the target stimuli,or to release the action by switching to the cancellation stimuli, basedupon a comparison between the hold-release classifier value and thefirst and second training classifier thresholds (blocks 512 and 514).

That is, method 500 may include one or more processors comparing thecalculated hold-release classifier value (block 510) to the calculatedfirst and second training classifier thresholds (block 504) to determinewhether the user has decide to hold an action associated with the targetstimuli (block 512). In various embodiments, method 500 may include theidentification of the hold and release states using any suitablecombination of the three rules as previously discussed with reference toFIG. 3B, for example, to establish whether a selected test answer image(or any other suitable stimulus that may be implemented with theidentification of hold and release states) should be held (kept) orreleased (cancelled) (block 512).

For example, the determination of whether the user has decided to holdthe action associated with the original target stimuli may be determined(block 512) when the calculated hold-release classifier value (block510) is greater than the first training classifier threshold (block504).

To provide another example, the determination that the user has decidednot to hold the action associated with the target stimuli may bedetermined (block 512) when the calculated hold-release classifier value(block 510) is less than the second training classifier threshold (block504).

If it is determined that the user has decided to hold the actionassociated with the target stimuli (block 512), then method 500 revertsto continuing to receive EEG signals and calculating hold-releaseclassifiers (block 510). If it is determined that the user has notdecided to hold the action associated with the target stimuli (block512), then method 500 continues (block 514).

In various embodiments, once the determination that the user has decidedto hold the action associated with the original target stimuli is made,method 500 may include generating additional hold-release classifiervalues (block 510) based on the most recent brain activity monitoringand then comparing the new hold-release classifier value to a previouslygenerated hold-release classifier and/or to the first and/or secondclassifier thresholds (block 512). In various embodiments, thecomparisons of more than one hold-release classifier value may beimplemented using any suitable number of rules as previously discussedwith reference to FIG. 3B, for example.

Various embodiments include repeating the acts of calculating the holdrelease classifier value (block 510) and determining if the user hasdecided to hold the action associated with the target stimuli (block512). In this way, method 500 may facilitate the continuousdetermination of whether to maintain the holding state or to switch to arelease state (blocks 510 and 512).

Method 500 may include one or more processors identifying a switch fromthe previous target stimuli to a release stimuli to release the actionrepresented by the hold state (block 514). This may include, forexample, the determination that a user changed her concentration fromone particular stimuli (e.g., flickering and/or flashing) associatedwith holding a presented test answer image (e.g., maintainingconcentration on image 308) to another stimuli associated with therelease of the identified holding state (e.g., switching concentrationto cancellation image 312).

Once the switch is made from the previous target stimuli to a releasestimuli (block 514), method 500 may revert back to calculating the holdrelease classifier value (block 510). But, when this is done,embodiments include the association of the hold state switching to thecancellation stimuli, and vice-versa.

To provide an illustrative example, a user may be initially exposed to atarget stimuli (e.g., one presented with a test answer image) and thismaintained exposure may be associated with the holding state. Method 500may determine (block 512) that the user has intended to cancel the testquestion by switching his exposure from the target stimuli to thecancellation stimuli (cancellation image) (block 514). Once this occurs,the reversion to the calculation of the subsequent hold-releaseclassifier value (block 510) results in the association of the originaltarget stimuli (test question image) being switched to the cancellationstimuli (release state). This reversion also results in the associationof the original cancellation stimuli (cancellation image) being switchedto the target stimuli (hold state).

As a result, the release state is subsequently processed as the new holdstate, and vice-versa. If, after the reversion (block 514 to block 510),the user switched his focus back to a test question, the calculatedhold-release classifier value (block 510) would be used and thedetermination made that the user has not decided to hold the actionassociated with the cancellation image (block 512). This process mayrepeat, switching the hold and release states any suitable number oftimes until one or more conditions are met (end), which is furtherdiscussed below.

In some embodiments, the number of times this reversion process isrepeated (blocks 510, 512, and 514) may be limited. For example, thisreversion process may be repeated by monitoring the user's brainactivity over a predetermined period of time, over a threshold maximumnumber of loops, etc., in which case method 500 may end. Theseembodiments may be particularly useful when a determination of whetherthe user has decided to hold the action associated with the originaltarget stimuli needs to be made within a relatively short period oftime, such as in a testing environment, for example. In this way, method500 allows for brain activity to be monitored over several iterations todetermine whether a holding state is maintained, thereby providing anaccurate determination of the user's decisions.

In other embodiments, method 500 may continuously repeat the reversionprocess (blocks 510, 512, and 514) without necessarily ending. Theseembodiments may be particularly useful in implementations of a BCI usedfor control systems. For example, if the BCI was implemented as part ofa motorized wheelchair, then it may be preferable to associate theholding state with moving the wheelchair forward, continuouslymonitoring the user's brain activity until a release state (or a safetystop) is detected.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for usinga BCI and/or other suitable control interfaces through the disclosedprinciples herein. For example, although several embodiments have beenprovided throughout the disclosure relating to cognitive testing andwheelchair control implementations, various embodiments may include anysuitable type of application utilizing state changes. To provide aspecific example, a gaming application may be implemented utilizing thehold-release algorithms as discussed herein. The gaming application maypresent other suitable types of stimuli instead of test questions andcancellation images that are relevant to a particular gaming applicationThe hold-release process, as discussed throughout the disclosure, maythen be applied to determine whether the user is ready to select fromamong various presented stimuli, whether the user intends to maintain aselection, whether the user intends to cancel the selection, to switchthe selection to another stimuli, etc.

Thus, while particular embodiments and applications have beenillustrated and described, it is to be understood that the disclosedembodiments are not limited to the precise construction and componentsdisclosed herein. Various modifications, changes and variations, whichwill be apparent to those skilled in the art, may be made in thearrangement, operation and details of the method and apparatus disclosedherein without departing from the spirit and scope defined in theappended claims.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothingrequires that the operations be performed in the order illustrated.Structures and functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter of the present disclosure.

Additionally, certain embodiments are described herein as includinglogic or a number of components or modules. Modules may constituteeither software modules (e.g., code stored on a machine-readable medium)or hardware modules. A hardware module is tangible unit capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some cases, a hardware module may include dedicated circuitry orlogic that is permanently configured (e.g., as a special-purposeprocessor, such as a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC)) to perform certainoperations. A hardware module may also include programmable logic orcircuitry (e.g., as encompassed within a general-purpose processor orother programmable processor) that is temporarily configured by softwareto perform certain operations. It will be appreciated that the decisionto implement a hardware module in dedicated and permanently configuredcircuitry or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations.

Accordingly, the term hardware should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwaremodules are temporarily configured (e.g., programmed), each of thehardware modules need not be configured or instantiated at any oneinstance in time. For example, where the hardware modules comprise ageneral-purpose processor configured using software, the general-purposeprocessor may be configured as respective different hardware modules atdifferent times. Software may accordingly configure a processor, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware and software modules can provide information to, and receiveinformation from, other hardware and/or software modules. Accordingly,the described hardware modules may be regarded as being communicativelycoupled. Where multiple of such hardware or software modules existcontemporaneously, communications may be achieved through signaltransmission (e.g., over appropriate circuits and buses) that connectthe hardware or software modules. In embodiments in which multiplehardware modules or software are configured or instantiated at differenttimes, communications between such hardware or software modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware or software moduleshave access. For example, one hardware or software module may perform anoperation and store the output of that operation in a memory device towhich it is communicatively coupled. A further hardware or softwaremodule may then, at a later time, access the memory device to retrieveand process the stored output. Hardware and software modules may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a SaaS.For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., application program interfaces(APIs).)

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” or a “routine” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms, routines and operations involve physicalmanipulation of physical quantities. Typically, but not necessarily,such quantities may take the form of electrical, magnetic, or opticalsignals capable of being stored, accessed, transferred, combined,compared, or otherwise manipulated by a machine. It is convenient attimes, principally for reasons of common usage, to refer to such signalsusing words such as “data,” “content,” “bits,” “values,” “elements,”“symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like.These words, however, are merely convenient labels and are to beassociated with appropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,condition A or B is satisfied by any one of the following: A is true (orpresent) and B is false (or not present), A is false (or not present)and B is true (or present), and both A and B are true (or present).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

This detailed description is to be construed as an example only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

The particular features, structures, or characteristics of any specificembodiment may be combined in any suitable manner and in any suitablecombination with one or more other embodiments, including the use ofselected features without corresponding use of other features. Inaddition, many modifications may be made to adapt a particularapplication, situation or material to the essential scope and spirit ofthe present invention. It is to be understood that other variations andmodifications of the embodiments of the present invention described andillustrated herein are possible in light of the teachings herein and areto be considered part of the spirit and scope of the present invention.By way of example, and not limitation, the present disclosurecontemplates at least the following aspects:

1. A computer-implemented method for determining answers to a cognitiveassessment test, comprising:

displaying, by one or more processors, images corresponding tomultiple-choice answers for the cognitive assessment test;

receiving, by one or more processors, electroencephalograph (EEG)signals based upon a user's brain activity during administration of thecognitive assessment test;

determining, by one or more processors, whether the user intends todecide upon an answer from the multiple-choice answers based upon theEEG signals;

determining, by one or more processors, the user's answer from themultiple-choice answers based upon the EEG signals after it isdetermined that the user intends to decide upon the answer; and

verifying, by one or more processors, the user's answer based upon theEEG signals received after the user's answer has been determined.

2. The computer-implemented method of claim 1, wherein the acts ofreceiving the EEG signals, determining whether the user intends todecide upon an answer, determining the user's answer, and verifying theuser's answer are performed without motor or oral feedback provided bythe user.

3. The computer-implemented method of either claim 1 or claim 2, whereinthe act of determining the user's answer comprises:

determining the user's answer based upon the EEG signals received inresponse to the user paying attention to an image from among the imagescorresponding to multiple-choice answers, and wherein the act ofverifying the user's answer comprises:

verifying the user's answer based upon the EEG signals received inresponse to the user continuing to pay attention to the image after theuser's answer has been determined.

4. The computer-implemented method of any one of claims 1-3, wherein theact of determining whether the user intends to decide upon an answercomprises:

determining that the user intends to decide upon an answer when a timeris displayed indicating a time period for the user to decide upon ananswer.

5. The computer-implemented method of any one of claims 1-4, furthercomprising:

once the user's answer has been determined, modifying the imagescorresponding to the multiple-choice answers by maintaining the image ofthe determined answer while de-emphasizing images corresponding to theremaining answers; and

generating a cancellation image indicative of an option to allow theuser to cancel the determined answer when the user pays attention to thecancellation image, and wherein the act of verifying the user's answercomprises:

verifying the user's answer by determining whether the user is payingattention to the image of the selected answer or the cancellation imagebased on the received EEG signals.

6. The computer-implemented method of any one of claims 1-5, wherein thecognitive assessment test is a standardized test having a plurality oftest questions, the answers to which provide a test answer profile, andfurther comprising:

repeating the acts of displaying images, receiving EEG signals,determining whether the user intends to decide upon an answer,determining the user's answer, and verifying the user's answer for eachof the plurality of test questions to provide a user answer profile; and

formatting the user answer profile in accordance with the test answerprofile to facilitate grading of the standardized test.

7. A non-transitory, tangible computer-readable medium storingmachine-readable instructions for determining answers to a cognitiveassessment test that, when executed by a processor, cause the processorto:

display images corresponding to multiple-choice answers for thecognitive assessment test;

receive electroencephalograph (EEG) signals based upon a user's brainactivity during administration of the cognitive assessment test;

determine whether the user intends to decide upon an answer from themultiple-choice answers based upon the EEG signals;

determine the user's answer from the multiple-choice answers based uponthe EEG signals after it is determined that the user intends to decideupon the answer; and

verify the user's answer based upon the EEG signals received after theuser's answer has been determined.

8. The non-transitory, tangible computer-readable medium of claim 7,wherein the instructions to of receive the EEG signals, to determinewhether the user intends to decide upon an answer, to determine theuser's answer, and to verify the user's answer are executed by theprocessor without motor or oral feedback provided by the user.

9. The non-transitory, tangible computer-readable medium of either claim7 or claim 8, wherein the instructions to determine the user's answerfurther include instructions that, when executed by the processor, causethe processor to:

determine the user's answer based upon the EEG signals received inresponse to the user paying attention to an image from among the imagescorresponding to multiple-choice answers, and wherein the instructionsto verify the user's answer include instructions to:

verify the user's answer based upon the EEG signals received in responseto the user continuing to pay attention to the image after the user'sanswer has been determined.

10. The non-transitory, tangible computer-readable medium of any one ofclaims 7-9, wherein the instructions to determine whether the userintends to decide upon an answer further include instructions that, whenexecuted by the processor, cause the processor to:

determine that the user intends to decide upon an answer when a timer isdisplayed indicating a time period for the user to decide upon ananswer.

11. The non-transitory, tangible computer-readable medium of any one ofclaims 7-10, further including instructions that, when executed by theprocessor, cause the processor to:

once the user's answer has been determined, to modify the imagescorresponding to the multiple-choice answers by maintaining the image ofthe determined answer while de-emphasizing images corresponding to theremaining answers; and

generate a cancellation image indicative of an option to allow the userto cancel the determined answer when the user pays attention to thecancellation image, and wherein the instructions to verify the user'sanswer include instructions to:

verify the user's answer by determining whether the user is payingattention to the image of the selected answer or the cancellation imagebased on the received EEG signals.

12. The non-transitory, tangible computer-readable medium of any one ofclaims 7-11, wherein the cognitive assessment test is a standardizedtest having a plurality of test questions, the answers to whichproviding a test answer profile, further including instructions that,when executed by the processor, cause the processor to:

repeat the execution of instructions to display images, receive EEGsignals, determine whether the user intends to decide upon an answer,determine the user's answer, and verify the user's answer for each ofthe plurality of test questions to provide a user answer profile; and

format the user answer profile in accordance with the test answerprofile to facilitate grading of the standardized test.

13. A method implemented in a brain-computer interface (BCI) computer,comprising:

calculating, by one or more processors, a first and a second range ofclassifier values based upon a user's electroencephalograph (EEG)signals while the user is exposed to a target and to a cancellationstimuli, respectively;

calculating, by one or more processors, a first and a second trainingclassifier threshold to separate the first and the second range ofclassifier values from one another;

classifying, by one or more processors, received EEG signals while theuser is subsequently exposed to the target or the cancellation stimulias being within the first or the second range of classifier values basedupon the first and a second training classifier thresholds;

determining, by one or more processors, whether the user has beenexposed to the target stimuli or to the cancellation stimuli based uponthe classifying of the subsequently received EEG signals into one of thefirst or the second range of classifier values;

calculating, by one or more processors, a hold-release classifier valuebased upon EEG signals received after determining whether the user hasbeen subsequently exposed to the target stimuli or to the cancellationstimuli; and

identifying, by one or more processors, whether the user has decided tohold an action associated with the target stimuli or to release theaction by switching to the cancellation stimuli based on a comparisonbetween the hold-release classifier value and the first and secondtraining classifier thresholds.

14. The method of claim 13, further comprising:

executing, by one or more processors, one or more actions when it isdetermined that the user has decided to hold the action associated withthe target stimuli; and

stopping, by one or more processors, the execution of one or moreactions when it is determined that the user has decided to release theaction by switching to the cancellation stimuli.

15. The method of any one of claims 13-14, wherein the act ofidentifying whether the user decides to hold the action comprises:

identifying the user's decision to hold the action associated with thetarget stimuli when the hold-release classifier value is greater thanthe first training classifier threshold.

16. The method of any one of claims 13-15, wherein the act ofidentifying whether the user decides to release the action comprises:

identifying a user's decision to release the action associated with thetarget stimuli when the hold-release classifier value is less than thesecond training classifier threshold.

17. The method of any one of claims 13-16, further comprising:

generating an additional hold-release classifier value based upon EEGsignals received after the determination that the user has decided tohold the action associated with the target stimuli; and

determining that the user has decided to hold the action associated withthe target stimuli when:

the hold-release classifier value is greater than the additionalhold-release classifier value;

the hold-release classifier value and the additional hold-releaseclassifier value are both positive; and

the hold-release classifier value and the additional hold-releaseclassifier value are both less than the first training classifierthreshold.

18. A non-transitory, tangible computer-readable medium storingmachine-readable instructions for determining answers to a cognitiveassessment test that, when executed by a processor, cause the processorto:

calculate a first and a second range of classifier values based upon auser's electroencephalograph (EEG) signals while the user is exposed totarget and cancellation stimuli, respectively;

calculate a first and a second training classifier threshold to separatethe first and the second range of classifier values from one another;

classify received EEG signals while the user is subsequently exposed tothe target or the cancellation stimuli as being within the first or thesecond range of classifier values based upon the first and a secondtraining classifier thresholds;

determine whether the user has been exposed to the target stimuli or tothe cancellation stimuli based upon the classifying of the subsequentlyreceived EEG signals into one of the first or the second range ofclassifier values;

calculate a hold-release classifier value based upon EEG signalsreceived after determining whether the user has been subsequentlyexposed to the target stimuli or to the cancellation stimuli; and

identify whether the user has decided to hold an action associated withthe target stimuli or to release the action by switching to thecancellation stimuli based on a comparison between the hold-releaseclassifier value and the first and second training classifierthresholds.

19. The non-transitory, tangible computer-readable medium of claim 18,further including instructions that, when executed by the processor,cause the processor to:

execute one or more actions when it is determined that the user hasdecided to hold the action associated with the target stimuli; and

stop the execution of one or more actions when it is determined that theuser has decided to release the action by switching to the cancellationstimuli.

20. The non-transitory, tangible computer-readable medium of any ofclaims 18-19, wherein the instructions to identify whether the userdecides to hold the selected action further include instructions that,when executed by the processor, cause the processor to:

identify the user's decision to hold the action associated with thetarget stimuli when the hold-release classifier value is greater thanthe first training classifier threshold.

21. The non-transitory, tangible computer-readable medium of any ofclaims 18-20, wherein the instructions to identify whether the userdecides to release the selected action further include instructionsthat, when executed by the processor, cause the processor to:

identify a user's decision to release the action associated with thetarget stimuli when the hold-release classifier value is less than thesecond training classifier threshold.

22. The non-transitory, tangible computer-readable medium of any ofclaims 18-21, further including instructions that, when executed by theprocessor, cause the processor to:

generate an additional hold-release classifier value based upon EEGsignals received after the determination that the user has decided tohold the action associated with the target stimuli; and

determine that the user has decided to hold the action associated withthe target stimuli when:

the hold-release classifier value is greater than the additionalhold-release classifier value;

the hold-release classifier value and the additional hold-releaseclassifier value are both positive; and

the hold-release classifier value and the additional hold-releaseclassifier value are both less than the first training classifierthreshold.

What is claimed is:
 1. A computer-implemented method for determininganswers to a cognitive assessment test, the method comprising:presenting, by one or more processors, images corresponding tomultiple-choice answers for the cognitive assessment test; receiving, byone or more processors, electroencephalograph (EEG) signals based upon auser's brain activity during administration of the cognitive assessmenttest; determining, by one or more processors, whether the user intendsto decide upon an answer from the multiple-choice answers based upon theEEG signals; in response to determining that the user intends to decideupon an answer, waiting until a pre-determined time period expiresbefore determining the user's answer; determining, by one or moreprocessors, the user's answer from the multiple-choice answers bydetermining that the user is paying attention to an image of the answerbased upon the EEG signals; while the user is determined to be payingattention to the image of the answer, presenting a cancellation imageindicative of an option to allow the user to cancel the determinedanswer when the user pays attention to the cancellation image; andverifying, by one or more processors, the user's answer based upon theEEG signals received after the user's answer has been determined,wherein the act of verifying the user's answer comprises determining,based upon the received EEG signals, whether the user continues payingattention to the image of the determined answer while the cancellationimage is being presented.
 2. The computer-implemented method of claim 1,wherein the acts of receiving the EEG signals, determining whether theuser intends to decide upon an answer, determining the user's answer,and verifying the user's answer are performed without motor or oralfeedback provided by the user.
 3. The computer-implemented method ofclaim 1, wherein the act of determining whether the user intends todecide upon an answer comprises determining that the user intends todecide upon an answer while a timer is displayed indicating a timeperiod for the user to decide upon an answer.
 4. Thecomputer-implemented method of claim 1, wherein the cognitive assessmenttest is a standardized test having a plurality of test questions, theanswers to which provide a test answer profile, and further comprising:repeating the acts of presenting images, receiving EEG signals,determining whether the user intends to decide upon an answer,determining the user's answer, and verifying the user's answer for eachof the plurality of test questions to provide a user answer profile; andformatting the user answer profile in accordance with the test answerprofile to facilitate grading of the standardized test.
 5. Thecomputer-implemented method of claim 1, further comprising once theuser's answer has been determined, modifying the images corresponding tothe multiple-choice answers by (i) maintaining, in the same location andappearance as presented to the user for determination, the image of thedetermined answer as a maintained image on a display, and (ii) modifyingthe appearance of the images corresponding to the remaining answers onthe display to de-emphasize them in appearance.
 6. A non-transitory,tangible computer-readable medium storing machine-readable instructionsfor determining answers to a cognitive assessment test that, whenexecuted by a processor, cause the processor to: present imagescorresponding to multiple-choice answers for the cognitive assessmenttest; receive electroencephalograph (EEG) signals based upon a user'sbrain activity during administration of the cognitive assessment test;determine whether the user intends to decide upon an answer from themultiple-choice answers based upon the EEG signals; in response todetermining that the user intends to decide upon an answer, wait until apre-determined time period expires before determining the user's answer;determine the user's answer from the multiple-choice answers bydetermining that the user is paying attention to an image of the answerbased upon the EEG signals; while the user is determined to be payingattention to the image of the answer, present a cancellation imageindicative of an option to allow the user to cancel the determinedanswer when the user pays attention to the cancellation image; andverify the user's answer based upon the EEG signals received after theuser's answer has been determined, wherein the instructions to verifythe user's answer include instructions to determine, based on thereceived EEG signals, whether the user continues paying attention to theimage of the determined answer while the cancellation image is beingpresented.
 7. The non-transitory, tangible computer-readable medium ofclaim 6, wherein the instructions to receive the EEG signals, todetermine whether the user intends to decide upon an answer, todetermine the user's answer, and to verify the user's answer areexecuted by the processor without motor or oral feedback provided by theuser.
 8. The non-transitory, tangible computer-readable medium of claim6, wherein the instructions to determine whether the user intends todecide upon an answer further include instructions that, while executedby the processor, cause the processor to determine that the user intendsto decide upon an answer when a timer is displayed indicating a timeperiod for the user to decide upon an answer.
 9. The non-transitory,tangible computer-readable medium of claim 6, wherein the cognitiveassessment test is a standardized test having a plurality of testquestions, the answers to which providing a test answer profile, furtherincluding instructions that, when executed by the processor, cause theprocessor to: repeat the execution of instructions to present images,receive EEG signals, determine whether the user intends to decide uponan answer, determine the user's answer, and verify the user's answer foreach of the plurality of test questions to provide a user answerprofile; and format the user answer profile in accordance with the testanswer profile to facilitate grading of the standardized test.
 10. Thenon-transitory, tangible computer-readable medium of claim 6, whereinthe instructions, when executed by the processor, cause the processorto, once the user's answer has been determined, modify the imagescorresponding to the multiple-choice answers by (i) maintaining, in thesame location and appearance as presented to the user for determination,the image of the determined answer as a maintained image on a display,and (ii) modifying the appearance of the images corresponding to theremaining answers on the display to de-emphasize them in appearance. 11.A computer-implemented method for determining answers to a cognitiveassessment test, the method comprising: displaying, by one or moreprocessors, images corresponding to multiple-choice answers for thecognitive assessment test on a display; receiving, by one or moreprocessors, electroencephalograph (EEG) signals based upon a user'sbrain activity during administration of the cognitive assessment test;determining, by one or more processors, whether the user intends todecide upon an answer from the multiple-choice answers based upon theEEG signals; in response to determining that the user intends to decideupon an answer, waiting until a pre-determined time period expiresbefore determining the user's answer; determining, by one or moreprocessors, the user's answer from the multiple-choice answers bydetermining that the user is paying attention to an image of the answerbased upon the EEG signals; while the user is determined to be payingattention to the image of the answer, presenting on the display acancellation image indicative of an option to allow the user to cancelthe determined answer when the user pays attention to the cancellationimage; and verifying or cancelling, by one or more processors, theuser's answer based upon the EEG signals received after the user'sanswer has been determined, wherein the act of verifying or cancellingthe user's answer comprises determining, based upon the received EEGsignals, whether the user continues paying attention to the image of thedetermined answer or transitions to focusing on the cancellation image.12. The computer-implemented method of claim 11, wherein the acts ofreceiving the EEG signals, determining whether the user intends todecide upon an answer, determining the user's answer, and verifying theuser's answer are performed without motor or oral feedback provided bythe user.
 13. The computer-implemented method of claim 11, wherein theact of determining whether the user intends to decide upon an answercomprises determining that the user intends to decide upon an answerwhile a timer is displayed indicating a time period for the user todecide upon an answer.
 14. The computer-implemented method of claim 11,wherein the cognitive assessment test is a standardized test having aplurality of test questions, the answers to which provide a test answerprofile, and further comprising: repeating the acts of displayingimages, receiving EEG signals, determining whether the user intends todecide upon an answer, determining the user's answer, presenting acancellation image, and verifying or cancelling the user's answer foreach of the plurality of test questions to provide a user answerprofile; and formatting the user answer profile in accordance with thetest answer profile to facilitate grading of the standardized test. 15.A non-transitory, tangible computer-readable medium storingmachine-readable instructions for determining answers to a cognitiveassessment test that, when executed by a processor, cause the processorto: display images corresponding to multiple-choice answers for thecognitive assessment test on a display; receive electroencephalograph(EEG) signals based upon a user's brain activity during administrationof the cognitive assessment test; determine whether the user intends todecide upon an answer from the multiple-choice answers based upon theEEG signals; in response to determining that the user intends to decideupon an answer, wait until a pre-determined time period expires beforedetermining the user's answer; determine the user's answer from themultiple-choice answers by determining that the user is paying attentionto an image of the answer based upon the EEG signals; while the user isdetermined to be paying attention to the image of the answer, present onthe display a cancellation image indicative of an option to allow theuser to cancel the determined answer when the user pays attention to thecancellation image; and verify or cancel the user's answer based uponthe EEG signals received after the user's answer has been determined,wherein the instructions to verify or cancel the user's answer includeinstructions to determine, based on the received EEG signals, whetherthe user continues paying attention to the image of the determinedanswer or transitions to focusing on the cancellation image.
 16. Thenon-transitory, tangible computer-readable medium of claim 15, whereinthe instructions to receive the EEG signals, to determine whether theuser intends to decide upon an answer, to determine the user's answer,and to verify the user's answer are executed by the processor withoutmotor or oral feedback provided by the user.
 17. The non-transitory,tangible computer-readable medium of claim 15, wherein the instructionsto determine whether the user intends to decide upon an answer furtherinclude instructions that, when executed by the processor, cause theprocessor to determine that the user intends to decide upon an answerwhile a timer is displayed indicating a time period for the user todecide upon an answer.
 18. The non-transitory, tangiblecomputer-readable medium of claim 15, wherein the cognitive assessmenttest is a standardized test having a plurality of test questions, theanswers to which providing a test answer profile, further includinginstructions that, when executed by the processor, cause the processorto: repeat the execution of instructions to display images, receive EEGsignals, determine whether the user intends to decide upon an answer,determine the user's answer, present a cancellation image, and verify orcancel the user's answer for each of the plurality of test questions toprovide a user answer profile; and format the user answer profile inaccordance with the test answer profile to facilitate grading of thestandardized test.